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bioRxiv2025

Nest Ecology-associated Impacts of Wastewater on Wild Bee Microbiomes

Anthropogenic pollution affects environments differently depending on proximity to pollution source, exposure route, and species ecology. Thus, understanding organisms ecological role and exposure route to contaminants is central to assessing pollution impact. Treated municipal wastewater releases contaminants into waterways and alters microbial communities. Plants absorb contaminants and expose animals through foraging and nest-building activities. Nesting ecology differences of ground vs wood cavity-nesting bees offers insight into niche-specific susceptibility to pollution. Because contaminants bind to soil strongly, ground-nesting bees near wastewater are likely most impacted, while wood cavity-nesting bees likely less impacted since plants ability to uptake contaminants are species dependent. We compared gut microbiomes of directly exposed soil-nesting Halictus ligatus and indirectly exposed wood-nesting Ceratina spp. upstream/downstream of wastewater. We collected bees, flowers, and soil, and analyzed their bacteria microbiomes (16S rRNA). Wastewater altered ground-nesting H. ligatus microbiome >18 times greater than wood cavity-nesting Ceratina adults. Ceratina larvae and pollen provisions showed significant but smaller shifts. Conversely, soil and flower microbiomes remained stable, indicating higher resilience. These results demonstrate that exposure routes drive contaminants susceptibility, with animal-associated microbes most vulnerable. Because bees are important pollinators and biodiversity contributors, these disruptions point to broader ecological risks in increasingly contaminated landscapes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/675205v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@679a58org.highwire.dtl.DTLVardef@1c61700org.highwire.dtl.DTLVardef@101359org.highwire.dtl.DTLVardef@1664987_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2025

Combining artificial intelligence and local ecological knowledge to document the largest ever-counted Cape buffalo mega-herd

Large aggregations of wild mammals are declining globally, sometimes before they can be scientifically documented and their ecological value understood, although non-scientists may be aware of these phenomena. Video recording of wildlife by non-academics is becoming more frequent with increasing activities of humans in natural habitats. This opportunistically-collected material can document rare or ecologically-important events, such as large aggregations, and thus provide potentially valuable data for ecologists and conservationists. We used footage of a large herd of Cape buffalo recorded by a wildlife film producer in the Mababe Depression, northern Botswana, and applied automated detection and tracking techniques to count individuals as they moved across the video frame. To complement this demographic snapshot, we accessed local ecological knowledge from stakeholders to provide contextual information for a more complete understanding of the ecosystem processes potentially driving population movement and trends. Manual review of the automated count resulted in a minimum group size of 3676 buffalo and a total estimated herd size of 4131 (range 3828-4446), which matched herd size estimates provided by local stakeholders. To our knowledge, this is the largest documented group of Cape buffalo, which we identify as a mega-herd and recommend research into the costs, benefits and ecological consequences of forming such large groups. This study represents an opportunistic collaboration among wildlife filmmakers, computer scientists, lay experts and ecologists, and highlights the value of combining contributions from different fields to generate information that can be used in conservation practice.

ecology↗

bioRxiv2018

Accounting for location uncertainty in azimuthal telemetry data improves ecological inference

Characterizing animal space use is critical to understand ecological relationships. Despite many decades of using radio-telemetry to track animals and make spatial inference, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models. We describe a novel azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into ecological models. We evaluate the ATM with commonly used estimators in several study design scenarios using simulation. We also provide illustra-tive empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We found the ATM to have good performance and the only model that has appropriate measures of coverage. Ignoring animal location un-certainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. We demonstrate that home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.

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bioRxiv2018

Benefits And Limitations Of Three-Dimensional Printing Technology For Ecological Research

BackgroundEcological research often involves sampling and manipulating non-model organisms that reside in heterogeneous environments. As such, ecologists often adapt techniques and ideas from industry and other scientific fields to design and build equipment, tools, and experimental contraptions custom-made for the ecological systems under study. Three-dimensional (3D) printing provides a way to rapidly produce identical and novel objects that could be used in ecological studies, yet ecologists have been slow to adopt this new technology. Here, we provide ecologists with an introduction to 3D printing.\n\nResultsFirst, we give an overview of the ecological research areas in which 3D printing is predicted to be the most impactful and review current studies that have already used 3D printed objects. We then outline a methodological workflow for integrating 3D printing into an ecological research program and give a detailed example of a successful implementation of our 3D printing workflow for 3D printed models of the brown anole, Anolis sagrei, for a field predation study. After testing two print media in the field, we show that the models printed from the less expensive and more sustainable material (blend of 70% plastic and 30% recycled wood fiber) were just as durable and had equal predator attack rates as the more expensive material (100% virgin plastic).\n\nConclusionsOverall, 3D printing can provide time and cost savings to ecologists, and with recent advances in less toxic, biodegradable, and recyclable print materials, ecologists can choose to minimize social and environmental impacts associated with 3D printing. The main hurdles for implementing 3D printing - availability of resources like printers, scanners, and software, as well as reaching proficiency in using 3D image software - may be easier to overcome at institutions with digital imaging centers run by knowledgeable staff. As with any new technology, the benefits of 3D printing are specific to a particular project, and ecologists must consider the investments of developing usable 3D materials for research versus other methods of generating those materials.

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bioRxiv2018

Uncovering indirect interactions in bipartite ecological networks

Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about networks, we show that this shift is supported by quantitative analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight ecologically important differences missed by the current index paradigm. We extend this finding to empirical plant-pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need for a new paradigm for bipartite ecological networks: one incorporating indirect interactions.

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bioRxiv2018

A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation

Social insect colonies are capable of allocating their workforce in a decentralised fashion; addressing a variety of tasks and responding effectively to changes in the environment. This process is fundamental to their ecological success, but the mechanisms behind it remain poorly understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap we propose a game theoretical model of task allocation. Individuals are characterised by a trait that determines how they split their energy between two prototypical tasks: foraging and regulation. To be viable, a colony needs to learn to adequately allocate its workforce between these two tasks. We study two different processes: individuals can learn relying exclusively on their own experience, or by using the experiences of others via social learning. We find that social organisation can be determined by the ecology alone, irrespective of interaction details. Weakly specialised colonies in which all individuals tend to both tasks emerge when foraging is cheap; harsher environments, on the other hand, lead to strongly specialised colonies in which each individual fully engages in a single task. We compare the outcomes of self-organised task allocation with optimal group performance. Counter to intuition, strongly specialised colonies perform suboptimally, whereas the group performance of weakly specialised colonies is closer to optimal. Social interactions lead to important differences when the colony deals with dynamic environments. Colonies whose individuals rely on their own experience are more exible when dealing with change. Our computational model is aligned with mathematical predictions in tractable limits. This different kind of model is useful in framing relevant and important empirical questions, where ecology and interactions are key elements of hypotheses and predictions.

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bioRxiv2018

Identifying models of a pragmatic theory of ecology

Theoretical syntheses have the role of describing and guiding knowledge generation, and are usually done by enunciating the conceptual bases that guide research in a given field. In fields that develop axiomatically, the conceptual basis can be easily identified in the set of axioms guiding model building. However, ecology does not develop axiomatically but rather pragmatically, i.e., ecologists do not build models based on a predefined set of assumptions (axioms). They rather resort to any information that seems useful to learn about ecological phenomena. Therefore, a theoretical synthesis in ecology cannot rely on the enunciation of axioms; instead, it requires identifying what information and knowledge ecologists use (i.e., what they decide is useful to learn). Here we present an approach for producing theoretical syntheses based on the information/knowledge most frequently used to learn about the world. The approach consists of (i) defining a phenomenon of interest; (ii) defining a collective of scientists studying the phenomenon; (iii) surveying the scientific studies about the phenomenon published by this collective; (iv) identifying the most relevant publications used in these studies; (v) identifying how the studies refer to the most relevant publications; (vi) synthesizing what is being used by this collective to learn about the phenomenon. We implemented the approach in a case study on the phenomenon of ecological succession, defining the collective as the scientists currently studying succession. We identified three propositions that synthesize the views of the defined collective about succession. The theoretical synthesis revealed that there is no clear division between "classical" and "contemporary" succession models, and that neutral models are being used to explain successional patterns alongside with models based on niche assumptions.By implementing the pragmatic approach in a case study, we show that it can be successfully used to produce syntheses describing the conceptual bases of a field, which have the potential to guide knowledge generation. As such, these syntheses fulfil the roles ascribed to scientific theories in the epistemological literature.

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bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

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bioRxiv2018

Structured environments fundamentally alter dynamics and stability of ecological communities

The dynamics and stability of ecological communities are intimately linked with the specific interactions - like cooperation or predation - between constituent species. In microbial communities, like those found in soils or the mammalian gut, physical anisotropies produced by fluid flow and chemical gradients impact community structure and ecological dynamics, even in structurally isotropic environments. Though natural communities existing in physically unstructured environments is rare, the role of environmental structure in determining community dynamics and stability remains poorly studied. To address this gap, we used modified Lotka-Volterra simulations of competitive microbial communities to characterize the effects of surface structure on community dynamics. We find that environmental structure has profound effects on communities, in a manner dependent on the specific pattern of interactions between community members. For two mutually competing species, eventual extinction of one competitor is effectively guaranteed in isotropic environments. However, addition of environmental structure enables long-term coexistence of both species via local pinning of competition interfaces, even when one species has a significant competitive advantage. In contrast, while three species competing in an intransitive loop (as in a game of rock-paper-scissors) coexist stably in isotropic environments, structural anisotropy disrupts the spatial patterns on which coexistence depends, causing chaotic population fluctuations and subsequent extinction cascades. These results indicate that the stability of microbial communities strongly depends on the structural environment in which they reside. Therefore, a more complete ecological understanding, including effective manipulation and interventions in natural communities of interest, must account for the physical structure of the environment.\n\nSIGNIFICANCEMany microbial communities of ecological and medical importance reside in complex and heterogeneous environments, such as soils or intestinal tracts. While many studies consider the effects of flow or chemical gradients in structuring these communities, how the physical structure of the environment shapes community dynamics and outcomes remains poorly understood. Using simulations of competitive microbial communities, we show that stability and dynamics qualitatively shift in environments with complex surface structures compared to open isotropic environments. Therefore, in addition to biochemical interactions between species, our work suggests that the physical structure of the environment is an equally important determinant of dynamics and stability in microbial communities, in a manner dependent on the specific patterns of interactions within that community.

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bioRxiv2018

A fish rots from the head down: how to use the leading digits of ecological data to detect their falsification.

Managing wildlife populations requires good data. Researchers and policy makers need reliable population estimates and, in case of commercial or recreational harvesting, also trustworthy information about the number of removed individuals. However, auditing schemes are often weak and political or economic pressure could lead to data fabrication or falsification. Time-series data and population models are crucial to detect anomalies, but they are not always available nor feasible. Therefore, researchers need other tools to identify suspicious patterns in ecological and environmental data, to prioritize their controls. We showed how the Benfords law might be used to identify anomalies and potential manipulation in ecological data, by testing for the goodness-of-fit of the leading digits with the Benfords distribution. For this task, we inspected two datasets that were found to be falsified, containing data about estimated large carnivore populations in Romania and Soviet commercial whale catches in the Pacific Ocean. In both the two datasets, the first and second digits numerical series deviated from the expected Benfords distribution. In data about large carnivores, the first too digits, taken together, also deviated from the expected Benfords distribution and were characterized by a high Mean Absolute Deviation. In Soviet whale catches, while the single digits deviated from the Benfords distribution and the Mean Absolute Deviation was high, the first two digits were not anomalous. This controversy invites researchers to combine multiple measures of nonconformity and to be cautious in analyzing mixtures of data. Testing the distribution of the leading digits might be a very useful tool to inspect ecological datasets and to detect potential falsifications, with great implications for policymakers and researchers as well. For example, if policymakers revealed anomalies in harvesting data or population estimates, commercial or recreational harvesting could be suspended and controls strengthened. On the other hand, revealing falsification in ecological research would be crucial for evidence-based conservation, as well as for research evaluation.

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bioRxiv2018

Improved spatial ecological sampling using open data and standardization: an example from malaria mosquito surveillance

Vector-borne disease control relies on efficient vector surveillance, mostly carried out using traps whose number and locations are often determined by expert opinion rather than a rigorous quantitative sampling design. In this work we first propose a framework for ecological sampling design which in its preliminary stages can take into account environmental conditions obtained from open data (i.e. remote sensing and meteorological stations). These environmental data are used to delimit the area into ecologically homogenous strata. By employing a model-based sampling design, the traps are deployed among the strata using a mixture of random and grid locations which allows balancing predictions and fitting accuracies. Sample sizes and the effect of ecological strata on sample sizes are estimated from previous sampling campaigns. Notably, we found that a configuration of 30 locations with 4 households each (120 traps) will have a similar accuracy in the estimates of mosquito abundance as 300 random samples. In addition, we show that random sampling independently from ecological strata, produces biased estimates of the mosquito abundance. Finally, we propose standardizing reporting of sampling designs to allow transparency and repetition / re-use in subsequent sampling campaigns.

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bioRxiv2018

Ecological networks: Pursuing the shortest path, however narrow and crooked

Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. Besides providing a compact representation of the interconnections between agents, network analysis allows the identification of especially important nodes, according to various metrics that often rely on the calculation of the shortest paths connecting any two nodes. While the interpretation of a shortest paths is straightforward in binary, unweighted networks, whenever weights are reported, the calculation could yield unexpected results. We analyzed 129 studies of ecological networks published in the last decade and making use of shortest paths, and discovered a methodological inaccuracy related to the edge weights used to calculate shortest paths (and related centrality measures), particularly in interaction networks. Specifically, 49% of the studies do not report sufficient information on the calculation to allow their replication, and 61% of the studies on weighted networks may contain errors in how shortest paths are calculated. Using toy models and empirical ecological data, we show how to transform the data prior to calculation and illustrate the pitfalls that need to be avoided. We conclude by proposing a five-point check-list to foster best-practices in the calculation and reporting of centrality measures in ecology and evolution studies.

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bioRxiv2019

Community size affects the signals of selection and ecological drift on biodiversity

Ecological drift can override the effects of deterministic niche selection on small populations and drive the assembly of small communities. We tested the hypothesis that smaller local communities are more dissimilar among each other because of ecological drift than larger communities, which are mainly structured by niche selection. We used a unique, comprehensive dataset on insect communities sampled identically in a total of 200 streams in climatically different regions (Brazil and Finland) that differ in community size by fivefold. Null models allowed us to estimate the magnitude to which beta diversity deviates from the expectation under a random assembly process while taking differences in species richness and relative abundance into account, i.e., beta deviation. Beta diversity of small tropical communities was consistently higher but closer to null expectations than {beta}-diversity of large communities. However, although {beta}-deviation and community size were strongly related in both regions, the direction of the relationship varied according to dissimilarity metrics. While incidence-based {beta}-diversity was lower than expected (communities were less dissimilar than null expectations) and negatively related to community size in Brazil, abundance-based {beta}-diversity was higher than expected (communities were more dissimilar than null expectations) and positively related to community size in both regions. We suggest that ecological drift drives variation in small communities by increasing the chances of species with low abundance and narrow distribution to occur within the metacommunity. Also, while weak niche selection and high dispersal rates likely reduced variation in community structure among large tropical streams, niche selection was likely sufficient to cause non-random variations in the relative abundances of genera among large communities in both regions. Habitat destruction, overexploitation, pollution, and reductions in connectivity have been reducing the size of biological communities. These environmental pressures will make smaller communities more vulnerable to novel conditions and render community dynamics more unpredictable, as random demographic processes should prevail under these conditions. Incorporation of community size into ecological models should provide conceptual, empirical and applied insights into a better understanding of the processes driving changes in biodiversity.

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bioRxiv2019

Case-control design identifies ecological drivers of endemic coral diseases

Endemic disease transmission is an important ecological process that is challenging to study because of low occurrence rates. Here, we investigate the ecological drivers of two coral diseases -- growth anomalies and tissue loss -- affecting five coral species. We first show that a statistical framework called the case-control study design, commonly used in epidemiology but rarely applied to ecology, provided high predictive accuracy (67-82%) and disease detection rates (60-83%) compared with a traditional statistical approach that yielded high accuracy (98-100%) but low disease detection rates (0-17%). Using this framework, we found evidence that 1) larger corals have higher disease risk; 2) shallow reefs with low herbivorous fish abundance, limited water motion, and located adjacent to watersheds with high fertilizer and pesticide runoff promote low levels of growth anomalies, a chronic coral disease; and 3) wave exposure, stream exposure, depth, and low thermal stress are associated with tissue loss disease risk during interepidemic periods. Variation in risk factors across host-disease pairs suggests that either different pathogens cause the same gross lesions in different species or that the same disease may arise in different species under different ecological conditions.

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bioRxiv2019

Energy landscape analysis of ecological communities elucidates the phase space of community assembly dynamics

Compositional multistability is widely observed in multispecies ecological communities. Since differences in community composition often lead to differences in community function, understanding compositional multistability is essential to comprehend the role of biodiversity in maintaining ecosystems. In community assembly studies, it has long been recognized that the order and timing of species migration and extinction influence structure and function of communities. The study of multistability in ecology has focused on the change in dynamical stability across environmental gradients, and was developed mainly for low-dimensional systems. As a result, methodologies for studying the compositional stability of empirical multispecies communities are not well developed. Here, we show that models previously used in ecology can be analyzed from a new perspective - the energy landscape - to unveil compositional stability in observational data. To show that our method can be applicable to real-world ecological communities, we simulated assembly dynamics driven by population level processes, and show that results were mostly robust to different simulation assumptions. Our method reliably captured the change in the overall compositional stability of multispecies communities over environmental change, and indicated a small fraction of community compositions that may be channels for transitions between stable states. When applied to murine gut microbiota, our method showed the presence of two alternative states whose relationship changes with age, and suggested mechanisms by which aging affects the compositional stability of the murine gut microbiota. Our method provides a practical tool to study the compositional stability of communities in a changing world, and will facilitate empirical studies that integrate the concept of multistability from different fields.

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bioRxiv2019

Deviations from dynamic equilibrium in ecological communities worldwide

Ecological communities are assembled by colonization and extinction events, that may be regulated by ecological niches1-5. The most parsimonious explanation of local community assembly is the Dynamic Equilibrium (DE) model, which assumes that community dynamics is shaped by random colonization and extinctions events, effectively ignoring the effects of niches1, 6. Despite its empirical success in explaining diversity patterns1, 5, 7, it is unknown to what extent the assembly dynamics of communities around the globe are consistent with this model. Using a newly developed methodology, we show that in 4989 communities from 49 different datasets, representing multiple taxa, biomes and locations, changes in richness and composition are larger than expected by DE. All the fundamental assumptions of DE are violated, but the large changes in species richness and composition primarily stem from the synchrony in the dynamics of different species. These results indicate that temporal changes in communities are predominantly driven by shared responses of co-occurring species to environmental changes, rather than by inter-specific competition. This finding is in sharp contrast to the long-term recognition of competition as a primary driver of species assembly8-12. While ecological niches are often thought to stabilize species diversity and composition4, 13, 14, we found that they promote large changes in ecological communities.

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bioRxiv2025

Measuring and modeling ecological rates with neutral theory

The pace of change in ecological communities is central to classic and emerging problems in paleobiology and ecology. Yet measuring rates of change in community composition is problematic: when measured as ecological distance divided by time, rates appear faster when measured over shorter time intervals, preventing straightforward interpretation of rates when timescales vary. Similar problems arise when richness or sample size varies. We develop an approach that mitigates these issues using Hubbells neutral theory, a model in which species abundances change through stochastic births and deaths in a community with a fixed number of individuals J. Change in community composition is faster for smaller J, so 1/J provides a process-based rate metric that can be estimated from empirical time-series. We derive the likelihood for changes in community composition under neutral theory. Our approach accurately estimates rates in simulated data even when timescale, richness, and sample size vary. We use Quaternary pollen assemblage timeseries to demonstrate the power of this method for comparing rates between sites and through time and testing for rate shifts. Our approach can be flexibly adapted to test hypotheses on the causes of ecological change, including tests of coordinated stasis, neutral theory, and the timing of anthropogenic change.

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bioRxiv2025

Towards a multi-metric assessment of river restoration ecological outcomes across projects and contexts

The assessment of the ecological outcomes of river restoration projects remains a complex undertaking due to the high variability of responses observed. This variability is dependent on a number of factors, including the location of the site under study, the indicators and taxa utilised in the analysis. In this study, a multi-metric and multi-contextual approach was applied to analyse 62 river restoration projects located in France, Germany and the Netherlands. This methodological approach facilitated the characterisation of the responses exhibited by macroinvertebrate communities in response to restoration initiatives, and the subsequent identification of the environmental contexts and the categories of restoration measures that exerted influence on these responses. Utilising log-response ratios based on both taxonomic and functional ecological metrics, a k-means clustering analysis was performed. This analysis yielded two distinct groups of sites: the first exhibiting predominantly positive responses to restoration, and the second demonstrating neutral or marginally negative responses. Positive responses have been shown to be associated with ambitious restoration projects, which are characterised by measures targeting regional-scale processes. These measures include the implementation of projects designed to improve river longitudinal continuity in degraded catchment areas, where the density of obstacles and the intensity of agricultural use are particularly high. Conversely, restoration initiatives targeting local-scale issues within more natural contexts or at high altitude exhibited minimal to no ecological response. This study emphasises the significance of an integrated approach that incorporates various facets (taxonomic and functional) of biodiversity, along with a meticulous examination of the environmental context and project construction, to enhance the evaluation of the ecological benefits of river restoration. The findings of this study may also be applicable in the context of fieldwork, providing practitioners with a framework to inform the design of restoration projects.

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